QC Ware and Goldman Sachs announce breakthrough in quantum algorithm

0


QC Ware, the privately-held Palo Alto-based quantum computing firm whose investors include Airbus Ventures, as well as financial services giants Citigroup, Goldman Sachs and DE Shaw, has announced a major breakthrough in the algorithmic calculations used to evaluate price Financial assets. In an announcement on Thursday, QC Ware said the algorithms it developed with Goldman Sachs outperformed classic Monte Carlo simulations historically used to model financial market prices and risks. The company says these algorithms will be usable on short-term hardware that is expected to be on the market within the next 5-10 years.

Monte Carlo methods involve complex calculations that are time consuming and computationally intensive and (hence) are typically performed by investment firms only once overnight. In volatile and rapidly changing market conditions, this can lead to algorithms giving outdated results.

Quantum computing, emerging computing, is believed to allow faster computation of huge data sets, performing Monte Carlo simulations 1000 times faster than classical methods. The problem is that these algorithms can only be executed using quantum hardware with error correction, which would be another 10 to 20 years in terms of hardware development. Current quantum devices have prohibitive error rates and can only perform a few computational steps before returning inaccurate results. Over the past year, QC Ware and Goldman Sachs have grappled with the question of how to cut the timeline of quantum hardware in half, while gaining speed.

The companies chose to increase the simulation speed of the classic Monte Carlo simulation to a factor of 100, rather than 1000, so that these calculations could be performed on available hardware much earlier. According to QC Ware, this could allow market simulations to run throughout the day, closer to real time.

“Goldman Sachs and QC Ware research teams have taken a new approach to designing Monte Carlo algorithms by trading in performance acceleration for reduced error rates, ”said Iordanis Kerenidis, Global Algorithm Manager at QC Ware. “Through rigorous analysis and empirical simulations, we have demonstrated that our Shallow Monte Carlo algorithms can lead to Monte Carlo simulations on quantum hardware that could be available in 5 to 10 years. “

“Quantum computing could have a significant impact on financial services, and our new work with QC Ware is bringing that future closer,” said William Zeng, head of quantum research at Goldman Sachs. “To do this, we have introduced new extensions to a basic technique of quantum algorithms. This illustrates the fundamental contributions our group seeks to make in the field of quantum technology.

Zeng said the Goldman team remains focused on developing “the best technology for the company and our customers.”

Aviation!

Earlier this week. QC Ware has announced that it is working with the US Air Force Research Laboratory (AFRL) to use one of QC Ware’s quantum machine learning algorithms (q-means) to understand the purpose of the mission unmanned aircraft by observing the flight path of the craft. It is believed that q-means (used in grouping and classification) can be applied to several AFRL mission applications.

The project is part of a larger AFRL effort to engage expert researchers from industry, academia and the Department of Defense to apply quantum information science to military concerns. air and space force and ensure that they remain the most advanced and competent force in the world.

“AFRL is pleased to partner with QC Ware for the development of quantum machine learning algorithms. The early and continued investment in quantum software aligns firmly with AFRL’s quantum strategy. As quantum computing hardware continues to advance rapidly and become more practical, we believe these types of algorithms will easily find applications in real Air Force scenarios, ”said Dr. Mike Hayduk, Deputy Director of the AFRL Information Directorate.



Source link

Leave A Reply

Your email address will not be published.